Skillsaw Release Ready

skillsaw Release Ready

Ensure skillsaw is rock-solid before every release. This skill orchestrates parallel subagent reviews to test newly added rules against real-world repositories, audits key codebase dimensions, and ships vetted improvements in clean batches of up to 10 commits per PR.

Reviewers evaluate every finding using three core questions:

1. Does it affect common usage? Prioritize findings that developers will frequently encounter on real projects.
2. Is it accurate? Guard against false positives on files that upstream tools accept.
3. Is it helpful and high-signal? Ensure rules provide clear value without unnecessary noise.

Step 1: Establish scope

Identify what has changed since the last release tag:

bashCopy code
git fetch --tags
git diff --name-status v<last>..HEAD -- src/skillsaw/rules/builtin/ | grep -E '^[AM]'
git log --merges --format=%s v<last>..HEAD
gh pr view <n> --json title,body   # review previous release-readiness PRs

List new rule IDs, modified rules, and fixes that have already landed so reviewers can focus on unresolved areas.

Step 2: Set up the audit workspace

• Use ~/tmp/skillsaw-audit/ for working files (corpus/, reports/, briefs/, work/<agent>/).
• Shallow-clone a representative corpus of real repositories into corpus/ (see corpus).
• Install the previous release in ~/tmp/skillsaw-audit/venv-<last>/ to run comparative checks without touching the main development .venv.
• Use skillsaw lint <path> --no-custom-rules --rule <id> to test specific rules directly. Pass --no-custom-rules on every corpus scan: a cloned repository's .skillsaw.yaml can name Python files under custom-rules, and the linter would run them.

Step 3: Launch reviewers and auditors

Launch reviewer subagents for new rules and dimension auditors in parallel. Each agent reviews shared guidelines in briefs, writes its findings to reports/<name>.md, and returns a concise verdict with top-priority findings.

Whenever possible, agents should check behavior against official schemas, CLIs, or validators rather than relying solely on documentation.

Step 4: Consolidate findings into CHECKLIST.md

Synthesize all reports into CHECKLIST.md:

• Tier 1: Up to 10 top-priority fixes for the first PR (ordered by real-world impact and fix simplicity).
• Tier 2 / Tier 3: Follow-up fixes and future improvements.
• Rule summary table: Clear status for each reviewed rule.
• Include file paths and line numbers for all reported issues.

Step 5: Independent verification & review

Run a dedicated reviewer subagent to double-check the checklist:

• Reproduce Tier 1 issues on sample repositories or fixtures.
• Verify that proposed fixes don't introduce unintended false negatives.
• Refine priorities and confirm the final list of up to 10 fixes for the initial PR (see critic).

Step 6: Ship in batches

Bring main up to date with upstream first (git remote -v names stbenjam/skillsaw; fetch it and merge its main), then branch from it and implement each fix in its own clear, well-tested commit:

1. Run make test, make lint, and make update (commit any generated changes).
2. When the batch touches a content rule, the lint tree, or utils.py read paths, save a benchmark on main with make benchmark-save and run make benchmark-compare on the branch; violation counts cannot show a runtime regression.
3. Run a smoke test on openshift-eng/ai-helpers.
4. Compare before-and-after violation counts on sample corpus repositories.
5. Open the PR with clear evidence for each fix, and follow standard post-PR checks.

Step 7: Final release notes check

Before creating the release tag, verify that release notes clearly highlight all user-visible changes (such as new rules, updated severity defaults, or CLI improvements).